Month: August 2018

from sklearn import svm, metrics
import random, re

lines = open('iris.csv', 'r', encoding='utf-8').read().split("\n")
f_tonum = lambda n : float(n) if re.match(r'^[0-9\.]+$', n) else n
f_cols = lambda li: list(map(f_tonum, li.strip().split(',')))
csv = list(map(f_cols, lines))
del csv[0]
random.shuffle(csv)

K = 5
csvk = [ [] for i in range(K) ]
for i in range(len(csv)):
	csvk[i % K].append(csv[i])

def split_data_label(rows):
	data = []; label = []
	for row in rows:
		data.append(row[0:4])
		label.append(row[4])
	return (data, label)

def calc_score(test, train):
	test_f, test_l = split_data_label(test)
	train_f, train_l = split_data_label(train)
	clf = svm.SVC()
	clf.fit(train_f, train_l)
	pre = clf.predict(test_f)
	return metrics.accuracy_score(test_l, pre)

score_list = []
for testc in csvk:
	trainc = []
	for i in csvk:
		if i != testc: trainc += i
	sc = calc_score(testc, trainc)
	score_list.append(sc)
print("各正解率=", score_list)
print("平均成果率=", sum(score_list) / len(score_list))

各正解率= [0.9666666666666667, 1.0, 1.0, 0.9333333333333333, 1.0]
平均成果率= 0.9800000000000001

import pandas as pd
from sklearn import cross_validation, svm, metrics
from sklearn.grid_search import GridSearchCV

train_csv = pd.read_csv("./mnist/train.csv")
test_csv = pd.read_csv("./mnist/t10k.csv")

train_label = train_csv.ix[:, 0]
train_data = train_csv.ix[:, 1:577]
test_label = test_csv.ix[:, 0]
test_data = test_csv.ix[:, 1:577]
print("学習データ数=", len(train_label))

params = [
	{"C": [1,10,100,1000], "kernel":["linear"]},
	{"C": [1,10,100,1000], "kernel":["rbf"], "gamma":[0.001, 0.0001]}
]

clf = GridSearchCV(svm.SVC(), params, n_jobs = -1)
clf.fit(train_data, train_label)
print("学習器=", clf.best_estimator_)

pre = clf.predict(test_data)
ac_score = metrics.accuracy_score(pre, test_label)
print("正解率=", ac_score)

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation, metrics

mr = pd.read_csv("mushroom.csv", header=None)

label = []
data = []
attr_list = []
for row_index, row in mr.iterrows():
	label.append(row.ix[0])
	row_data = []
	for v in row.ix[1:]:
		row_data.append(ord(v))
	data.append(row_data)

data_train, data_test, label_train, label_test = \
	cross_validation.train_test_split(data, label)

clf = RandomForestClassifier()
clf.fit(data_train, label_train)

predict = clf.predict(data_test)

ac_score = metrics.accuracy_score(label_test, predict)
cl_report = metrics.classification_report(label_test, predict)
print("正解率=", ac_score)
print("レポート=\n", cl_report)

[vagrant@localhost python]$ python3 app.py
正解率= 1.0
レポート=
precision recall f1-score support

e 1.00 1.00 1.00 1031
p 1.00 1.00 1.00 1000

avg / total 1.00 1.00 1.00 2031

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation, metrics

mr = pd.read_csv("mushroom.csv", header=None)

label = []
data = []
attr_list = []
for row_index, row in mr.iterrorws():
	label.append(row.ix[0])
	exdata = []
	for col, v in enumerate(row.ix[1:]):
		if row_index == 0:
			attr = {"dic": {}, "cnt":0}
			attr_list.append(attr)
		else:
			attr = attr_list[col]
		d = [0,0,0,0,0,0,0,0,0,0,0,0]
		if v in attr["dic"]:
			idx = attr["dic"][v]
		else:
			idx = attr["cnt"]
			attr["dic"][v] = idx
			attr["cnt"] += 1
			d[idx] = 1
			exdata += d
		data.append(exdata)

data_train, data_test, label_train, label_test = \
	cross_validation.train_test_split(data, label)

clf = RandomForestClassifier()
clf.fit(data_train, label_train)
predict = clf.predict(data_test)
ac_score = metrics.accuracy_score(label_test, predict)
print("正解率=", ac_score)

import urllib.request as req
local = "mushroom.csv"
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data"
req.urlretrieve(url, local)
print("ok")

import random

def calc_bmi(h, w):
bmi = w / (h / 100) ** 2
if bmi < 18.5: return "thin" if bmi < 25: return "normal" return "fat" fp = open("bmi.csv", "w", encoding="utf-8") fp.write("height, weight, label\r\n") cnt = {"thin":0, "normal":0, "fat":0} for i in range(20000): h = random.randint(120, 200) w = random.randint(35, 80) label = calc_bmi(h, w) cnt[label] += 1 fp.write("{0},{1},{2}\r\n".format(h, w, label)) fp.close() print("ok", cnt) [/python] [python] from sklearn import cross_validation, svm, metrics import matplotlib.pyplot as plt import pandas as pd tbl = pd.read_csv("bmi.csv") label = tbl["label"] w = tbl["weight"] / 100 h = tbl["height"] / 200 wh = pd.concat([w, h], axis=1) data_train, data_test, label_train, label_test = \ cross_validation.train_test_split(wh, label) clf = svm.SVC() clf.fit(data_train, label_train) predict = clf.predict(data_test) ac_score = metrics.accuracy_score(label_test, predict) cl_report = metrics.classification_report(label_test, predict) print("正解率=", ac_score) print("レポート=\n", cl_report) [/python] [python] import matplotlib.pyplot as plt import pandas as pd tbl = pd.read_csv("bmi.csv", index_col=2) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) def scatter(lbl, color): b = tbl.loc[lbl] ax.scatter(b["weight"],b["height"], c=color, label=lbl) scatter("fat", "red") scatter("normal", "yellow") scatter("thin", "purple") ax.legend() plt.savefig("bmi-test.png") [/python]